OpenRaiser/Intern-Atlas huggingface dataset:
CloudScan - A configuration-free invoice analysis system using recurrent neural networks
Venue: arXiv cs.CL 2017
Year: 2017

We present CloudScan; an invoice analysis system that requires zero configuration or upfront annotation. In contrast to previous work, CloudScan does not rely on templates of invoice layout, instead it learns a single global model of invoices that naturally generalizes to unseen invoice layouts. The model is trained using data automatically extracted from end-user provided feedback. This automatic training data extraction removes the requirement for users to annotate the data precisely. We describe a recurrent neural network model that can capture long range context and compare it to a baseline logistic regression model corresponding to the current CloudScan production system. We train and evaluate the system on 8 important fields using a dataset of 326,471 invoices. The recurrent neural network and baseline model achieve 0.891 and 0.887 average F1 scores respectively on seen invoice layouts. For the harder task of unseen invoice layouts, the recurrent neural network model outperforms the baseline with 0.840 average F1 compared to 0.788.
State Space Modeling of Inverter Based Microgrids Considering Distributed Secondary Voltage Control
Venue: arXiv cs.SY 2017
Year: 2017

Appropriate control of high penetration renewable energies in power systems requires a complete modeling of the system. In this paper, a comprehensive state space modeling of voltage source inverters, networks and loads are studied. We have modeled a secondary voltage control that utilizes the nominal set points of the output voltage as the input for the controller design. A distributed control algorithm only requiring a sparse communication between neighboring distributed generators (DGs) is used. The proposed secondary voltage model is applied on a microgrid with three DGs and two loads to verify the results.
Audio signal encryption using chaotic Hénon map and lifting wavelet transforms
Venue: arXiv cs.CR 2017
Year: 2017

We propose a new audio signal encryption scheme based on the chaotic Hénon map. The scheme mainly comprises two phases: one is the preprocessing stage where the audio signal is transformed into a data by the lifting wavelet scheme and the other in which the transformed data is encrypted by chaotic data set and hyperbolic functions. Furthermore, we use dynamic keys and consider the key space size to be large enough to resist any kind of cryptographic attacks. A statistical investigation is also made to test the security and the efficiency of the proposed scheme.
Objective Classes for Micro-Facial Expression Recognition
Venue: arXiv cs.CV 2017
Year: 2017

Micro-expressions are brief spontaneous facial expressions that appear on a face when a person conceals an emotion, making them different to normal facial expressions in subtlety and duration. Currently, emotion classes within the CASME II dataset are based on Action Units and self-reports, creating conflicts during machine learning training. We will show that classifying expressions using Action Units, instead of predicted emotion, removes the potential bias of human reporting. The proposed classes are tested using LBP-TOP, HOOF and HOG 3D feature descriptors. The experiments are evaluated on two benchmark FACS coded datasets: CASME II and SAMM. The best result achieves 86.35\% accuracy when classifying the proposed 5 classes on CASME II using HOG 3D, outperforming the result of the state-of-the-art 5-class emotional-based classification in CASME II. Results indicate that classification based on Action Units provides an objective method to improve micro-expression recognition.
Efficient barycentric point sampling on meshes
Venue: arXiv cs.GR 2017
Year: 2017

We present an easy-to-implement and efficient analytical inversion algorithm for the unbiased random sampling of a set of points on a triangle mesh whose surface density is specified by barycentric interpolation of non-negative per-vertex weights. The correctness of the inversion algorithm is verified via statistical tests, and we show that it is faster on average than rejection sampling.
Leaf Counting with Deep Convolutional and Deconvolutional Networks
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we investigate the problem of counting rosette leaves from an RGB image, an important task in plant phenotyping. We propose a data-driven approach for this task generalized over different plant species and imaging setups. To accomplish this task, we use state-of-the-art deep learning architectures: a deconvolutional network for initial segmentation and a convolutional network for leaf counting. Evaluation is performed on the leaf counting challenge dataset at CVPPP-2017. Despite the small number of training samples in this dataset, as compared to typical deep learning image sets, we obtain satisfactory performance on segmenting leaves from the background as a whole and counting the number of leaves using simple data augmentation strategies. Comparative analysis is provided against methods evaluated on the previous competition datasets. Our framework achieves mean and standard deviation of absolute count difference of 1.62 and 2.30 averaged over all five test datasets.
Design of Configurable Sequential Circuits in Quantum-dot Cellular Automata
Venue: arXiv cs.ET 2017
Year: 2017

Quantum-dot cellular automata (QCA) is a likely candidate for future low power nano-scale electronic devices. Sequential circuits in QCA attract more attention due to its numerous application in digital industry. On the other hand, configurable devices provide low device cost and efficient utilization of device area. Since the fundamental building block of any sequential logic circuit is flip flop, hence constructing configurable, multi-purpose QCA flip-flops are one of the prime importance of current research. This work proposes a design of configurable flip-flop (CFF) which is the first of its kind in QCA domain. The proposed flip-flop can be configured to D, T and JK flip-flop by configuring its control inputs. In addition, to make more efficient configurable flip-flop, a clock pulse generator (CPG) is designed which can trigger all types of edges (falling, rising and dual) of a clock. The same CFF design is used to realize an edge configurable (dual/rising/falling) flip- flop with the help of CPG. The biggest advantage of using edge configurable (dual/rising/falling) flip-flop is that it can be used in 9 different ways using the same single circuit. All the proposed designs are verified using QCADesigner simulator.
Design of Adiabatic MTJ-CMOS Hybrid Circuits
Venue: arXiv cs.ET 2017
Year: 2017

Low-power designs are a necessity with the increasing demand of portable devices which are battery operated. In many of such devices the operational speed is not as important as battery life. Logic-in-memory structures using nano-devices and adiabatic designs are two methods to reduce the static and dynamic power consumption respectively. Magnetic tunnel junction (MTJ) is an emerging technology which has many advantages when used in logic-in-memory structures in conjunction with CMOS. In this paper, we introduce a novel adiabatic hybrid MTJ/CMOS structure which is used to design AND/NAND, XOR/XNOR and 1-bit full adder circuits. We simulate the designs using HSPICE with 32nm CMOS technology and compared it with a non-adiabatic hybrid MTJ/CMOS circuits. The proposed adiabatic MTJ/CMOS full adder design has more than 7 times lower power consumtion compared to the previous MTJ/CMOS full adder.
Integral Curvature Representation and Matching Algorithms for Identification of Dolphins and Whales
Venue: arXiv cs.CV 2017
Year: 2017

We address the problem of identifying individual cetaceans from images showing the trailing edge of their fins. Given the trailing edge from an unknown individual, we produce a ranking of known individuals from a database. The nicks and notches along the trailing edge define an individual's unique signature. We define a representation based on integral curvature that is robust to changes in viewpoint and pose, and captures the pattern of nicks and notches in a local neighborhood at multiple scales. We explore two ranking methods that use this representation. The first uses a dynamic programming time-warping algorithm to align two representations, and interprets the alignment cost as a measure of similarity. This algorithm also exploits learned spatial weights to downweight matches from regions of unstable curvature. The second interprets the representation as a feature descriptor. Feature keypoints are defined at the local extrema of the representation. Descriptors for the set of known individuals are stored in a tree structure, which allows us to perform queries given the descriptors from an unknown trailing edge. We evaluate the top-k accuracy on two real-world datasets to demonstrate the effectiveness of the curvature representation, achieving top-1 accuracy scores of approximately 95% and 80% for bottlenose dolphins and humpback whales, respectively.
Modular Learning Component Attacks: Today's Reality, Tomorrow's Challenge
Venue: arXiv cs.CR 2017
Year: 2017

Many of today's machine learning (ML) systems are not built from scratch, but are compositions of an array of {\em modular learning components} (MLCs). The increasing use of MLCs significantly simplifies the ML system development cycles. However, as most MLCs are contributed and maintained by third parties, their lack of standardization and regulation entails profound security implications. In this paper, for the first time, we demonstrate that potentially harmful MLCs pose immense threats to the security of ML systems. We present a broad class of {\em logic-bomb} attacks in which maliciously crafted MLCs trigger host systems to malfunction in a predictable manner. By empirically studying two state-of-the-art ML systems in the healthcare domain, we explore the feasibility of such attacks. For example, we show that, without prior knowledge about the host ML system, by modifying only 3.3{\textperthousand} of the MLC's parameters, each with distortion below $10^{-3}$, the adversary is able to force the misdiagnosis of target victims' skin cancers with 100\% success rate. We provide analytical justification for the success of such attacks, which points to the fundamental characteristics of today's ML models: high dimensionality, non-linearity, and non-convexity. The issue thus seems fundamental to many ML systems. We further discuss potential countermeasures to mitigate MLC-based attacks and their potential technical challenges.
The Parallel Algorithm for the 2-D Discrete Wavelet Transform
Venue: arXiv cs.CV 2017
Year: 2017

The discrete wavelet transform can be found at the heart of many image-processing algorithms. Until now, the transform on general-purpose processors (CPUs) was mostly computed using a separable lifting scheme. As the lifting scheme consists of a small number of operations, it is preferred for processing using single-core CPUs. However, considering a parallel processing using multi-core processors, this scheme is inappropriate due to a large number of steps. On such architectures, the number of steps corresponds to the number of points that represent the exchange of data. Consequently, these points often form a performance bottleneck. Our approach appropriately rearranges calculations inside the transform, and thereby reduces the number of steps. In other words, we propose a new scheme that is friendly to parallel environments. When evaluating on multi-core CPUs, we consistently overcome the original lifting scheme. The evaluation was performed on 61-core Intel Xeon Phi and 8-core Intel Xeon processors.
Exploit imaging through opaque wall via deep learning
Venue: arXiv cs.NE 2017
Year: 2017

Imaging through scattering media is encountered in many disciplines or sciences, ranging from biology, mesescopic physics and astronomy. But it is still a big challenge because light suffers from multiple scattering is such media and can be totally decorrelated. Here, we propose a deep-learning-based method that can retrieve the image of a target behind a thick scattering medium. The method uses a trained deep neural network to fit the way of mapping of objects at one side of a thick scattering medium to the corresponding speckle patterns observed at the other side. For demonstration, we retrieve the images of a set of objects hidden behind a 3mm thick white polystyrene slab, the optical depth of which is 13.4 times of the scattering mean free path. Our work opens up a new way to tackle the longstanding challenge by using the technique of deep learning.
RaspiReader: An Open Source Fingerprint Reader Facilitating Spoof Detection
Venue: arXiv cs.CV 2017
Year: 2017

We present the design and prototype of an open source, optical fingerprint reader, called RaspiReader, using ubiquitous components. RaspiReader, a low-cost and easy to assemble reader, provides the fingerprint research community a seamless and simple method for gaining more control over the sensing component of fingerprint recognition systems. In particular, we posit that this versatile fingerprint reader will encourage researchers to explore novel spoof detection methods that integrate both hardware and software. RaspiReader's hardware is customized with two cameras for fingerprint acquisition with one camera providing high contrast, frustrated total internal reflection (FTIR) images, and the other camera outputting direct images. Using both of these image streams, we extract complementary information which, when fused together, results in highly discriminative features for fingerprint spoof (presentation attack) detection. Our experimental results demonstrate a marked improvement over previous spoof detection methods which rely only on FTIR images provided by COTS optical readers. Finally, fingerprint matching experiments between images acquired from the FTIR output of the RaspiReader and images acquired from a COTS fingerprint reader verify the interoperability of the RaspiReader with existing COTS optical readers.
The Asymmetric Colonel Blotto Game
Venue: arXiv cs.GT 2017
Year: 2017

This paper explores the Nash equilibria of a variant of the Colonel Blotto game, which we call the Asymmetric Colonel Blotto game. In the Colonel Blotto game, two players simultaneously distribute forces across $n$ battlefields. Within each battlefield, the player that allocates the higher level of force wins. The payoff of the game is the proportion of wins on the individual battlefields. In the asymmetric version, the levels of force distributed to the battlefields must be nondecreasing. In this paper, we find a family of Nash equilibria for the case with three battlefields and equal levels of force and prove the uniqueness of the marginal distributions. We also find the unique equilibrium payoff for all possible levels of force in the case with two battlefields, and obtain partial results for the unique equilibrium payoff for asymmetric levels of force in the case with three battlefields.
Ultra-Dense Networks: A New Look at the Proportional Fair Scheduler
Venue: arXiv cs.NI 2017
Year: 2017

In this paper, we theoretically study the proportional fair (PF) scheduler in the context of ultra-dense networks (UDNs). Analytical results are obtained for the coverage probability and the area spectral efficiency (ASE) performance of dense small cell networks (SCNs) with the PF scheduler employed at base stations (BSs). The key point of our analysis is that the typical user is no longer a random user as assumed in most studies in the literature. Instead, a user with the maximum PF metric is chosen by its serving BS as the typical user. By comparing the previous results of the round-robin (RR) scheduler with our new results of the PF scheduler, we quantify the loss of the multi-user diversity of the PF scheduler with the network densification, which casts a new look at the role of the PF scheduler in UDNs. Our conclusion is that the RR scheduler should be used in UDNs to simplify the radio resource management (RRM).
Mining the Demographics of Political Sentiment from Twitter Using Learning from Label Proportions
Venue: arXiv cs.SI 2017
Year: 2017

Opinion mining and demographic attribute inference have many applications in social science. In this paper, we propose models to infer daily joint probabilities of multiple latent attributes from Twitter data, such as political sentiment and demographic attributes. Since it is costly and time-consuming to annotate data for traditional supervised classification, we instead propose scalable Learning from Label Proportions (LLP) models for demographic and opinion inference using U.S. Census, national and state political polls, and Cook partisan voting index as population level data. In LLP classification settings, the training data is divided into a set of unlabeled bags, where only the label distribution in of each bag is known, removing the requirement of instance-level annotations. Our proposed LLP model, Weighted Label Regularization (WLR), provides a scalable generalization of prior work on label regularization to support weights for samples inside bags, which is applicable in this setting where bags are arranged hierarchically (e.g., county-level bags are nested inside of state-level bags). We apply our model to Twitter data collected in the year leading up to the 2016 U.S. presidential election, producing estimates of the relationships among political sentiment and demographics over time and place. We find that our approach closely tracks traditional polling data stratified by demographic category, resulting in error reductions of 28-44% over baseline approaches. We also provide descriptive evaluations showing how the model may be used to estimate interactions among many variables and to identify linguistic temporal variation, capabilities which are typically not feasible using traditional polling methods.
Biocompatible Writing of Data into DNA
Venue: arXiv cs.ET 2017
Year: 2017

A simple DNA-based data storage scheme is demonstrated in which information is written using "addressing" oligonucleotides. In contrast to other methods that allow arbitrary code to be stored, the resulting DNA is suitable for downstream enzymatic and biological processing. This capability is crucial for DNA computers, and may allow for a diverse array of computational operations to be carried out using this DNA. Although here we use gel-based methods for information readout, we also propose more advanced methods involving protein/DNA complexes and atomic force microscopy/nano-pore schemes for data readout.
Status of Serverless Computing and Function-as-a-Service(FaaS) in Industry and Research
Venue: arXiv cs.DC 2017
Year: 2017

This whitepaper summarizes issues raised during the First International Workshop on Serverless Computing (WoSC) 2017 held June 5th 2017 and especially in the panel and associated discussion that concluded the workshop. We also include comments from the keynote and submitted papers. A glossary at the end (section 8) defines many technical terms used in this report.
Anomaly Detection in Wireless Sensor Networks
Venue: arXiv cs.LG 2017
Year: 2017

Wireless sensor networks usually comprise a large number of sensors monitoring changes in variables. These changes in variables represent changes in physical quantities. The changes can occur for various reasons; these reasons are highlighted in this work. Outliers are unusual measurements. Outliers are important; they are information-bearing occurrences. This work seeks to identify them based on an approach presented in [1]. A critical review of most previous works in this area has been presented in [2], and few more are considered here just to set the stage. The main work can be described as this; given a set of measurements from sensors that represent a normal situation, [1] proceeds by first estimating the probability density function (pdf) of the set using a data-split approach, then estimate the entropy of the set using the arithmetic mean as an approximation for the expectation. The increase in entropy that occurs when strange data is recorded is used to detect unusual measurements in the test set depending on the desired confidence interval or false alarm rate. The results presented in [1] have been confirmed for different test signals such as the Gaussian, Beta, in one dimension and beta in two dimensions, and a beta and uniform mixture distribution in two dimensions. Finally, the method was confirmed on real data and the results are presented. The major drawbacks of [1] were identified, and a method that seeks to mitigate this using the Bhattacharyya distance is presented. This method detects more subtle anomalies, especially the type that would pass as normal in [1]. Finally, recommendations for future research are presented: the subject of interpretability, especially for subtle measurements, being the most elusive as of today.
An Ensemble Framework for Detecting Community Changes in Dynamic Networks
Venue: arXiv cs.SI 2017
Year: 2017

Dynamic networks, especially those representing social networks, undergo constant evolution of their community structure over time. Nodes can migrate between different communities, communities can split into multiple new communities, communities can merge together, etc. In order to represent dynamic networks with evolving communities it is essential to use a dynamic model rather than a static one. Here we use a dynamic stochastic block model where the underlying block model is different at different times. In order to represent the structural changes expressed by this dynamic model the network will be split into discrete time segments and a clustering algorithm will assign block memberships for each segment. In this paper we show that using an ensemble of clustering assignments accommodates for the variance in scalable clustering algorithms and produces superior results in terms of pairwise-precision and pairwise-recall. We also demonstrate that the dynamic clustering produced by the ensemble can be visualized as a flowchart which encapsulates the community evolution succinctly.
One-Shot Concept Learning by Simulating Evolutionary Instinct Development
Venue: arXiv cs.CV 2017
Year: 2017

Object recognition has become a crucial part of machine learning and computer vision recently. The current approach to object recognition involves Deep Learning and uses Convolutional Neural Networks to learn the pixel patterns of the objects implicitly through backpropagation. However, CNNs require thousands of examples in order to generalize successfully and often require heavy computing resources for training. This is considered rather sluggish when compared to the human ability to generalize and learn new categories given just a single example. Additionally, CNNs make it difficult to explicitly programmatically modify or intuitively interpret their learned representations. We propose a computational model that can successfully learn an object category from as few as one example and allows its learning style to be tailored explicitly to a scenario. Our model decomposes each image into two attributes: shape and color distribution. We then use a Bayesian criterion to probabilistically determine the likelihood of each category. The model takes each factor into account based on importance and calculates the conditional probability of the object belonging to each learned category. Our model is not only applicable to visual scenarios, but can also be implemented in a broader and more practical scope of situations such as Natural Language Processing as well as other places where it is possible to retrieve and construct individual attributes. Because the only condition our model presents is the ability to retrieve and construct individual attributes such as shape and color, it can be applied to essentially any class of visual objects.